Assignment
jacob Moutouama
1/30/2020
knitr::opts_chunk$set(error = TRUE)rm(list=ls(all=TRUE)) ## Clear all
setwd("/Users/jmoutouama/Documents/EEBCourse/PhyloMeth/PhyloMeth2020")Assignment: PhyloMeth
Week1: : Getting Started
#install.packages("ctv") #the CRAN task view package
#library(ctv) #to load the package
#install.views(c("Phylogenetics", "WebTechnologies"))
#Bioconductor is a separate repository with lots of packages for dealing with genetic data, especially nextgen data. To install this and a few initial packages:
#if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install(version = "3.10")
#BiocManager::install("Biostrings", ask=FALSE)
##Experience with coding.
##Function to plot any order in R.
GetTrees <- function(Order) {
library(ape)
X <- c("red", "orange", "yellow", "green", "blue", "purple")
result<-plot(Order, type = "c", use.edge.length = FALSE,
edge.color = sample(X, length(bird.orders$edge)/2, replace = TRUE),
edge.width = rep(5, length(bird.orders$edge)/2))
segments(rep(0, 6), 6.5:1.5, rep(2, 6), 6.5:1.5, lwd = 5, col = X)
text(rep(2.5, 6), 6.5:1.5, paste(X, "..."), adj = 0)
title("Fancy tree...")
return(result)
}
#let's use the data in ape
library(ape)## Warning: package 'ape' was built under R version 3.5.2 data("bird.orders")
GetTrees(bird.orders)
## $type
## [1] "cladogram"
##
## $use.edge.length
## [1] FALSE
##
## $node.pos
## [1] 2
##
## $node.depth
## [1] 1
##
## $show.tip.label
## [1] TRUE
##
## $show.node.label
## [1] FALSE
##
## $font
## [1] 3
##
## $cex
## [1] 1
##
## $adj
## [1] 0
##
## $srt
## [1] 0
##
## $no.margin
## [1] FALSE
##
## $label.offset
## [1] 0
##
## $x.lim
## [1] 0.0000 36.8954
##
## $y.lim
## [1] 1 23
##
## $direction
## [1] "rightwards"
##
## $tip.color
## [1] "black"
##
## $Ntip
## [1] 23
##
## $Nnode
## [1] 22
##
## $root.time
## NULL
##
## $align.tip.label
## [1] FALSEWeek2: Getting Trees and Data
## Let's use the fonction made by Brian to do so. Here I want to make a tree from Open Tree of Life
source("Tree from Open Source.R")
GetTreeFromOpenTree("Thunbergia")
## [1] "The taxon tree has 15 terminals and 1 internal nodes out of 13 possible, which means it is 0% resolved"# I got here just one node. Therefore, my tree had no information
## Let's make a tree from a studies
#install.packages("devtools")
#devtools::install_github("ropensci/rotl")
library("rotl")
## Now we can run the function with P_class as input. Here p_class is any class of species (Eg. Mammalia)
GetTreeFromOpenTree <- function(p_class) {
library(rotl)
library(ape)
p_class.studies <- studies_find_studies(property="ot:focalCladeOTTTaxonName",
value=p_class)
print(p_class.studies)
p_class.studies.ids <- unlist(p_class.studies$study_ids)
p_class.study1.metadata <- get_study_meta(p_class.studies[[1]][[1]])
print(get_publication(p_class.study1.metadata))
study_id1<-p_class.studies.ids[1]
study_tree1<-list_trees(p_class.studies,p_class.studies.ids[1])[1]
p_class.tree<-get_tree_ids( p_class.study1.metadata)
p_class.study1.tree1 <- get_study_tree(study_id1, study_tree1)
plot.phylo(p_class.study1.tree1, type="fan", cex=0.2)
return.list <- list(p_class.tree, p_class.study1.tree1 )
names(return.list) <- c("p_class.tree", "study1.tree")
return(return.list)
}
GetTreeFromOpenTree("Mammalia")## study_ids n_trees tree_ids
## 1 pg_2582 1 tree5988
## 2 pg_2647 2 tree6169, tree6170
## 3 pg_2812 2 tree6545, tree6546
## 4 ot_1366 1 Tr98763
## 5 ot_308 3 tree1, tree2, tree3
## 6 pg_1428 2 tree2855, tree2856
## 7 ot_109 1 tree1
## 8 pg_2550 10 tree5513, tree5515, tree5516, tree5517, tree5518, ...
## 9 ot_755 1 tree1
## candidate study_year
## 1 2006
## 2 tree6169 2013
## 3 tree6545 2012
## 4 2017
## 5 tree1 2007
## 6 tree2855 2011
## 7 tree1 2012
## 8 2004
## 9 tree1 2016
## title
## 1 A higher-level MRP supertree of placental mammals
## 2 The placental mammal ancestor and the post-K-Pg radiation of placentals
## 3 Joint reconstruction of divergence times and life-history evolution in placental mammals using a phylogenetic covariance model
## 4 'Waking the undead: Implications of a soft explosive model for the timing of placental mammal diversification'
## 5 The delayed rise of present-day mammals
## 6 Impacts of the Cretaceous Terrestrial Revolution and KPg Extinction on Mammal Diversification
## 7 Genome-scale coestimation of species and gene trees
## 8 "Building the mammalian sector of the tree of life: Combining different data and a discussion of divergence times for placental mammals
## 9 Multiple morphological clocks and total-evidence tip-dating in mammals
## study_doi
## 1 http://dx.doi.org/10.1186/1471-2148-6-93
## 2 http://dx.doi.org/10.1126/science.1229237
## 3 http://dx.doi.org/10.1111/j.1558-5646.2011.01558.x
## 4 http://dx.doi.org/10.1016/j.ympev.2016.09.017
## 5 http://dx.doi.org/10.1038/nature05634
## 6 http://dx.doi.org/10.1126/science.1211028
## 7 http://dx.doi.org/10.1101/gr.141978.112
## 8
## 9 http://dx.doi.org/10.1098/rsbl.2016.0033
## [1] "Beck, R.M.D., O.R.P Bininda-Emonds, M. Cardillo, F.-G. Liu, A. Purvis. 2006. A higher-level MRP supertree of placental mammals. BMC Evolutionary Biology 6: 93."
## attr(,"DOI")
## [1] "http://dx.doi.org/10.1186/1471-2148-6-93"
## $p_class.tree
## [1] "tree5988"
##
## $study1.tree
##
## Phylogenetic tree with 115 tips and 113 internal nodes.
##
## Tip labels:
## Abrocomidae, Agoutidae, Caviidae, Hydrochaeridae, Dasyproctidae, Capromyidae, ...
##
## Rooted; no branch lengths.Week4: Gene Tree Species Tree
devtools::install_github("bomeara/phybase")## Skipping install of 'phybase' from a github remote, the SHA1 (05949f49) has not changed since last install.
## Use `force = TRUE` to force installationlibrary(rotl)
library(ape)
phy <- get_study_tree("ot_485", "tree1")
plot(phy, cex=0.3)
library(geiger)## Warning: package 'geiger' was built under R version 3.5.2phy <- drop.random(phy, Ntip(phy) - 10)
plot(phy)
axisPhylo()
library(phybase)## Loading required package: Matrix##
## Attaching package: 'phybase'## The following objects are masked from 'package:ape':
##
## node.height, write.dnagene.tree <- phybase::sim.coaltree.phylo(phy, pop.size=1e-12)
plot(gene.tree)
library(phytools)## Loading required package: mapsplot(cophylo(phy, gene.tree, cbind(sort(phy$tip.label), sort(gene.tree$tip.label))))## Rotating nodes to optimize matching...
## Done.
## Rotating nodes to optimize matching...
## Done.
species.tree <- rcoal(7)
species.tree$edge.length <- species.tree$edge.length / (10*max(branching.times(species.tree)))
gene.tree <- phybase::sim.coaltree.phylo(species.tree)
plot(cophylo(species.tree, gene.tree, cbind(sort(species.tree$tip.label), sort(gene.tree$tip.label))))## Rotating nodes to optimize matching...
## Done.
Week 5: See RevBayes file in the file and the output
Week 6: DiscreteCharacters
# Data from Brandley et al. (2008)
library(ape)
library(phytools)
sqData<-read.csv("brandley_table.csv")
head(sqData)## Species SVL TL SE FLL HLL Fingers Toes
## 1 Agamodon anguliceps 74.7 8.3 3.1 0.0 0 0 0
## 2 Amphisbaena alba 474.0 40.1 12.6 0.0 0 0 0
## 3 Bipes biporus 167.2 16.0 3.6 6.1 0 5 0
## 4 Bipes caniculatus 182.5 29.6 4.5 6.5 0 4 0
## 5 Bipes tridactylus 127.5 36.0 3.7 6.0 0 3 0
## 6 Blanus cinereus 174.9 21.8 4.1 0.0 0 0 0sqTree<-read.nexus("squamate.tre.txt")#To get a character for “limbed” versus “limbless” we will have to create it by finding all species with all limbs of length 0.
hindLimbs<-sqData[,"HLL"]!=0
foreLimbs<-sqData[,"FLL"]!=0
limbless<-!hindLimbs & !foreLimbs
limbless## [1] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [13] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [133] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [181] FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [205] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
## [229] FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
## [241] TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE#Now we need to set our species names to match our tree, and assign these names to our trait vector:
speciesNames<-sqData[,1]
speciesNames## [1] Agamodon anguliceps Amphisbaena alba
## [3] Bipes biporus Bipes caniculatus
## [5] Bipes tridactylus Blanus cinereus
## [7] Chirindia swimmertoni Diplometopon zarudnyi
## [9] Geocalamus acutus Monopeltis capensis
## [11] Rhineura floridana Trogonophis wiegmanni
## [13] Abronia graminea Anguis fragilis
## [15] Anniella geronimensis Anniella pulchra
## [17] Barisia imbricata Celestus enneagrammus
## [19] Diploglossus bilobatus Diploglossus pleei
## [21] Elgaria coerulea Elgaria kingii
## [23] Elgaria multicarinata Elgaria panamintina
## [25] Elgaria paucicarinata Gerrhonotus liocephalus
## [27] Mesaspis moreleti Ophiodes striatus
## [29] Ophisaurus apodus Ophisaurus attenuatus
## [31] Ophisaurus harti Ophisaurus koellikeri
## [33] Ophisaurus ventralis Sauresia agasepsoides
## [35] Wetmorena haetiana Chamaesaura anguina
## [37] Cordylus cataphractus Cordylus cordylus
## [39] Cordylus jordani Cordylus warreni
## [41] Platysaurus rhodiseinsis Pseudocordylus microlepidotus
## [43] Dibamus novaeguineae Coleonyx elegans
## [45] Diplodactylus damaeus Gekko gecko
## [47] Gonatodes albogularis Oedura coggeri
## [49] Teratoscincus scincus Angolosaurus skoogi
## [51] Cordylosaurus trivittata Gerrhosaurus major
## [53] Gerrhosaurus nigrolineatus Tetradactylus africanus
## [55] Tetradactylus seps Tetradactylus tetradactylus
## [57] Tracheloptychus madagascariensis Zonosaurus ornatus
## [59] Alopoglossus atriventris Alopoglossus carinicaudatus
## [61] Alopoglossus copii Arthrosaura kockii
## [63] Arthrosaura reticulata Bachia bresslaui
## [65] Bachia dorbignyi Bachia flavescens
## [67] Calyptommatus leiolepis Calyptommatus nicterus
## [69] Calyptommatus sinebrachiatus Cercosaura argulus
## [71] Cercosaura eigenmanni Cercosaura ocellata
## [73] Cercosaura quadrilineata Cercosaura schreibersii
## [75] Colobodactylus dalcyanus Colobodactylus taunayi
## [77] Colobosaura modesta Ecpleopus affinis
## [79] Gymnophthalmus leucomystax Heterodactylus imbricatus
## [81] Iphisa elegans Leposoma percarinatum
## [83] Micrablepharus maximiliani Neusticurus ecpleopus
## [85] Neusticurus rudis Nothobachia ablephara
## [87] Pholidobolus montium Placosoma glabellum
## [89] Procellosaurinus erythrocercus Procellosaurinus tetradactylus
## [91] Proctoporus bolivianus Proctoporus simoterus
## [93] Psilophthalmus paeminosus Ptychoglossus brevifrontalis
## [95] Rhachisaurus brachylepis Tretioscincus agilis
## [97] Vanzosaura rubricauda Heloderma suspectum
## [99] Basiliscus basiliscus Calotes versicolor
## [101] Dipsosaurus dorsalis Enyaloides laticeps
## [103] Gambelia wislizenii Leiocephalus carinatus
## [105] Leiolepis belliana Phrynocephalus versicolor
## [107] Polychrus marmoratus Urosaurus ornatus
## [109] Meroles cuneirostris Podarcis sicula
## [111] Psammodromus hispanicus Takydromus smaragdinus
## [113] Lanthanotus borneensis Aprasia pseudopulchella
## [115] Aprasia pulchella Aprasia repens
## [117] Aprasia striolata Delma australis
## [119] Delma borea Delma fraseri
## [121] Delma grayi Delma impar
## [123] Delma inornata Delma molleri
## [125] Delma nasuta Delma tincta
## [127] Lialis burtoni Lialis jicari
## [129] Pletholax gracilis Pygopus lepidopus
## [131] Pygopus nigriceps Acontias litoralis
## [133] Acontias meleagris Acontias percivali
## [135] Acontophiops lineatus Amphiglossus astrolabi
## [137] Amphiglossus igneocaudatus Amphiglossus intermedius
## [139] Amphiglossus macrocercus Amphiglossus melanopleura
## [141] Amphiglossus melanurus Amphiglossus mouroundavae
## [143] Amphiglossus ornaticeps Amphiglossus punctatus
## [145] Amphiglossus stumpffi Amphiglossus tsaratananensis
## [147] Amphiglossus waterloti Anomalopus mackayi
## [149] Anomalopus swansoni Brachymeles gracilis
## [151] Brachymeles talinis Calyptotis scutirostrum
## [153] Chalcides chalcides Chalcides mionecton
## [155] Chalcides ocellatus Chalcides polylepis
## [157] Coeranoscincus reticulatus Ctenotus leonhardii
## [159] Ctenotus pantherinus Ctenotus robustus
## [161] Egernia whitii Eremiascincus richardsonii
## [163] Eugongylus rufescens Eulamprus amplus
## [165] Eulamprus murrayi Eulamprus quoyii
## [167] Eumeces schneideri Feylinia polylepis
## [169] Glaphyromorphus gracilipes Glaphyromorphus isolepis
## [171] Gnypetoscincus queenslandiae Gongylomorphus bojeri
## [173] Hakaria simonyi Hemiergis peroni
## [175] Janetaescincus braueri Lamprolepis smaragdina
## [177] Lerista bipes Lerista bougainvillii
## [179] Melanoseps occidentalis Mesoscincus managuae
## [181] Mesoscincus schwartzei Morethia butleri
## [183] Nangura spinosa Notoscincus ornatus
## [185] Ophiomorus punctatissimus Ophioscincus ophioscincus
## [187] Pamelaescincus gardineri Paracontias brocchii
## [189] Paracontias hildebrandti Paracontias holomelas
## [191] Plestiodon egregius Plestiodon elegans
## [193] Plestiodon fasciatus Plestiodon inexpectatus
## [195] Plestiodon laticeps Plestiodon longirostris
## [197] Plestiodon lynxe Plestiodon obsoletus
## [199] Plestiodon reynoldsi Prasinohaema virens
## [201] Proscelotes eggeli Pygomeles braconnieri
## [203] Saiphos equalis Scelotes anguineus
## [205] Scelotes arenicolus Scelotes bipes
## [207] Scelotes caffer Scelotes gronovii
## [209] Scelotes kasneri Scelotes mirus
## [211] Scelotes sexlineatus Scincopus fasciatus
## [213] Scincus scincus Sepsina angolensis
## [215] Sphenops boulengeri Sphenops sphenopsiformis
## [217] Tiliqua adelaidensis Tribolonotus gracilis
## [219] Typhlacontias brevipes Typhlacontias punctatissimus
## [221] Typhlosaurus caecus Typhlosaurus lineatus
## [223] Voeltzkowia fierinensis Voeltzkowia lineata
## [225] Acrochordus granulatus Agkistrodon contortrix
## [227] Anilius scytale Calabaria reinhardtii
## [229] Candoia carinata Casarea dussumieri
## [231] Causus maculatus Cereberus rhynchops
## [233] Charina bottae Chondropython viridis
## [235] Cylindrophis maculatus Elaphe guttata
## [237] Epicrates striatus Eryx johni
## [239] Exiliboa plicata Farancia abacura
## [241] Leptotyphlops humilis Loxocemus bicolor
## [243] Micrurus fulvius Nerodia sipedon
## [245] Pareas kuangtungensis Trachyboa boulengeri
## [247] Tropidophis haitianus Typhlops angolensis
## [249] Ungaliophis continentalis Uropeltis ceylonicus
## [251] Xenopeltis unicolor Shinisaurus crocodiluris
## [253] Sphenodon punctatus Ameiva ameiva
## [255] Aspidoscelis sexlineatus Dicrodon guttulatum
## [257] Teius teyou Varanus griseus
## [259] Lepidophyma flavimaculatum Xantusia vigilis
## [261] Xenosaurus grandis
## 261 Levels: Abronia graminea Acontias litoralis ... Zonosaurus ornatusspecies<-sub(" ","_",speciesNames)
#names(limbless)<-species#Now, we can change what we are calling our character values (although TRUE & FALSE would be fine):
limbs<-sapply(limbless,function(x) if(x) 0 else 1)
class (limbs)## [1] "numeric"## Now we create a data frame to clean these data
limbs.data<-data.frame(species,limbs)#library(devtools)
#devtools::install_github("phylotastic/rphylotastic")
#library(rphylotastic)
#library(geiger)
str(limbs)## num [1:261] 0 0 1 1 1 0 0 0 0 0 ...CleanData <- function(phy, data) {
library(rphylotastic)
speciesNames<-unlist(data[,1], use.names = FALSE)
cleaned.names.data<-taxa_resolve_names_with_gnr(speciesNames)
cleaned.names.phy<-taxa_resolve_names_with_gnr(phy$tip.label)
phy.cleaned.names<-phy
phy.cleaned.names$tip.label <- cleaned.names.phy
data.vector<-data[,2]
names(data.vector)<-cleaned.names.data
cleaned.data<-treedata(phy.cleaned.names,data.vector, sort=TRUE, warnings=TRUE)
return(cleaned.data)
}
cleaned.discrete<-CleanData(sqTree,limbs.data)##
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|======================================================================| 100%## Warning in treedata(phy.cleaned.names, data.vector, sort = TRUE, warnings = TRUE): The following tips were not found in 'phy' and were dropped from 'data':
## Gonatodes albogularis
## Lepidophyma flavimaculatum
## Trachyboa boulengericlass(cleaned.discrete[[2]])## [1] "matrix"VisualizeData <- function(phy, data) {
dotTree(phy,data[,1],colors=setNames(c("blue","red"),c(1,0)),type="fan",fsize=0.3,lwd=2)
}
VisualizeData(cleaned.discrete$phy,cleaned.discrete$data)
##First, let’s use parsimony to look at ancestral states:
library( phangorn)## Warning: package 'phangorn' was built under R version 3.5.2##
## Attaching package: 'phangorn'## The following objects are masked from 'package:phybase':
##
## treedist, upgmadata(Laurasiatherian)
cleaned.discrete.phydat<-as.phyDat(cleaned.discrete$data, type="USER", levels = c("1","0"), ambiguity="-")
cleaned.discrete.tree<-pml(cleaned.discrete$phy, cleaned.discrete.phydat)
anc.p <- ancestral.pars(cleaned.discrete$phy, cleaned.discrete.phydat, type = "MPR", cost = NULL,return = "prob")
plotAnc(cleaned.discrete.tree, anc.p, 1)
anc.ml <- ancestral.pml(pml(cleaned.discrete$phy, cleaned.discrete.phydat), type="ml")
plotAnc(cleaned.discrete.tree, anc.ml, 1)
## Yes the second tree differ from the first. it seems like the second tree has more precision. Week 6: Continuous character models
#install.packages("yearn")
library(ape)
library(geiger)
library(OUwie)## Warning: package 'OUwie' was built under R version 3.5.2## Loading required package: nloptr## Loading required package: latticelibrary(phytools)
#library(devtools)
#devtools::install_github("phylotastic/rphylotastic")
library(rphylotastic)## This data come from Brandley et al. (2008)
sqData<-read.csv("brandley_table.csv")
head(sqData)## Species SVL TL SE FLL HLL Fingers Toes
## 1 Agamodon anguliceps 74.7 8.3 3.1 0.0 0 0 0
## 2 Amphisbaena alba 474.0 40.1 12.6 0.0 0 0 0
## 3 Bipes biporus 167.2 16.0 3.6 6.1 0 5 0
## 4 Bipes caniculatus 182.5 29.6 4.5 6.5 0 4 0
## 5 Bipes tridactylus 127.5 36.0 3.7 6.0 0 3 0
## 6 Blanus cinereus 174.9 21.8 4.1 0.0 0 0 0sqTree<-read.nexus ("squamate.tre.txt")
plotTree(sqTree,type="fan",lwd=1,fsize=0.5)
source("resolvescientificnames.R")
CleanData <- function(phy, data) {
speciesNames<-unlist(data[,1], use.names = FALSE)
cleaned.names.data<-taxa_resolve_names_with_gnr(speciesNames)
cleaned.names.phy<-taxa_resolve_names_with_gnr(phy$tip.label)
phy.cleaned.names<-phy
phy.cleaned.names$tip.label <- cleaned.names.phy
data.vector<-data[,2]
names(data.vector)<-cleaned.names.data
cleaned.data<-treedata(phy.cleaned.names,data.vector, sort=TRUE, warnings=TRUE)
return(cleaned.data)
}
cleaned.data<-CleanData(sqTree,sqData)##
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|======================================================================| 100%## Warning in treedata(phy.cleaned.names, data.vector, sort = TRUE, warnings = TRUE): The following tips were not found in 'phy' and were dropped from 'data':
## Gonatodes albogularis
## Lepidophyma flavimaculatum
## Trachyboa boulengericlass(cleaned.data)## [1] "list"str(cleaned.data)## List of 2
## $ phy :List of 5
## ..$ edge : int [1:514, 1:2] 259 260 261 262 263 264 265 266 267 268 ...
## ..$ edge.length: num [1:514] 48.33 2.27 2.51 5.62 4.37 ...
## ..$ Nnode : int 257
## ..$ root.edge : num 0
## ..$ tip.label : chr [1:258] "Abronia graminea" "Acontias litoralis" "Acontias meleagris" "Acontias percivali" ...
## ..- attr(*, "class")= chr "phylo"
## ..- attr(*, "order")= chr "cladewise"
## $ data: num [1:258, 1] 96.2 114 197.9 207 166.9 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:258] "Abronia graminea" "Acontias litoralis" "Acontias meleagris" "Acontias percivali" ...
## .. ..$ : NULLVisualizeData <- function(phy, data) {
diagnostic<-contMap(phy, data, res=100, fsize=NULL, ftype=NULL, lwd=4, legend=NULL,
lims=NULL, outline=TRUE, sig=3, type="phylogram", direction="rightwards",
plot=FALSE)
plot(diagnostic)
}
VisualizeData(cleaned.data$phy,cleaned.data$data[,1])
## Brownian motion is a stochastic model in which changes from one time to the next are random draws from a normal distribution with mean 0.0 and variance σ2 × Δt. In other words, the expected variance under Brownian motion increases linearly through time with instantaneous rate σ2.
BM2<-fitContinuous(cleaned.data$phy,cleaned.data$data[,1],model="BM")
BM2## GEIGER-fitted comparative model of continuous data
## fitted 'BM' model parameters:
## sigsq = 138.341054
## z0 = 169.171661
##
## model summary:
## log-likelihood = -1502.985290
## AIC = 3009.970579
## AICc = 3010.017638
## free parameters = 2
##
## Convergence diagnostics:
## optimization iterations = 100
## failed iterations = 0
## frequency of best fit = 1.00
##
## object summary:
## 'lik' -- likelihood function
## 'bnd' -- bounds for likelihood search
## 'res' -- optimization iteration summary
## 'opt' -- maximum likelihood parameter estimates#Here we see that the rate of evolution is 138.341054 in 169.171661 unit of time. OU1 <- fitContinuous(cleaned.data$phy, cleaned.data$data[,1], model="OU")## Warning in fitContinuous(cleaned.data$phy, cleaned.data$data[, 1], model =
## "OU"): Non-ultrametric tree with OU model, using VCV method.## Warning in cache$dat - mu: Recycling array of length 1 in vector-array arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in cache$dat - mu: Recycling array of length 1 in vector-array arithmetic is deprecated.
## Use c() or as.vector() instead.par(mfcol=(c(1,2)))
plot(cleaned.data$phy, show.tip.label=FALSE)
ou.tree <- rescale(cleaned.data$phy, model="OU",0.001)
plot(ou.tree)
##These trees are slighly differents. The second tree vary according to the value of a. As a, the parameter used for transformation increase, the time for diversifation decreaseAIC.BM2 <- BM2$opt$aicc
AIC.OU1 <- OU1$opt$aicc#one.discrete.char <-limbs
#reconstruction.info <- ace(one.discrete.char, sqTree, type="discrete", method="ML", CI=TRUE)
#best.states <- colnames(reconstruction.info$lik.anc)[apply(reconstruction.info$lik.anc, 1, which.max)]